Image Processing Reference
extraction from an urban environment. Object-based approaches for identifying
and classifying land use objects are also being developed (Zhan et al. 2002 ; Zhan
2003 ). In the field of transportation, Tao et al. ( 1998 ) used an object-based
approach to create a road network database containing information on road sur-
face conditions for inspection and maintenance. Such approaches are also now
being explored to map and monitor informal areas and slums (Niebergall et al.
2007 ; Sliuzas et al. 2008 ).
Data Sources for Urban Applications
The usefulness of different approaches is highly dependent upon the data sources
that are available. Basically, four generations of sensors for urban studies can be
distinguished: first-generation low-resolution sensors such as LANDSAT MSS
(80m); second and third generation medium - to high-resolution sensors such as
LANDSAT TM (30m), SPOT 4 (10-20 m), SPOT 5 (5-10 m), or IRS (5.8-23 m);
and most recent, fourth-generation very high-resolution sensors such as IKONOS
and QUICKBIRD (1 m and less) (Donnay et al. 2001 ; Chapter 7 in this volume).
Since the availability of high and very high resolution sensors the interest for using
remote sensing data for urban application has increased (Ehlers 2002), because
these sensors now facilitate the identification of urban objects, such as individual
buildings and details of road networks (Brussel et al. 2003 ).
Considerable interest is also being shown in ultra-high resolution data from
airborne platforms, laser scanners, and digital cameras. For example, Small Format
Aerial Photography (SFAP) is used for rapid, low cost data capture (Sliuzas 2004 ).
Laser data is also used for obtaining a high-resolution digital terrain model (DTM),
including 3D-models of cities (Vosselman et al. 2005 ). Furthermore, the use of
laser data to detect changes on buildings and other urban objects has been explored
in the recent study of Steinle and Baehr ( 2002 ).
Selection of an Appropriate Resolution
One of the oldest but still useful schemes for considering the relationship between
the spatial resolution of remote sensing data and land use/land cover is that devel-
oped by Anderson et al. ( 1976 ) (Table 5.1 ). This scheme divides urban land uses
into four hierarchical levels and provides an
approximate indication of the sensor resolu-
tion required for a given land use/land cover
classification. Although this scheme continues
to be useful for many remote sensing users, it
is primarily concerned with general land use
and land cover classes. In contrast, the more
Anderson et al. ( 1976 )'s
scheme provides an
approximate indication of
the sensor spatial resolution
required for a given land
use/land cover classification